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一项关于社交距离对缓解新冠疫情的影响及效果的跨国和跨地区研究。

A Transnational and Transregional Study of the Impact and Effectiveness of Social Distancing for COVID-19 Mitigation.

作者信息

Filho Tarcísio M Rocha, Moret Marcelo A, Mendes José F F

机构信息

Instituto de Física and International Center for Condensed Matter Physics, Campus Universitário Darcy Ribeiro, Universidade de Brasília, Asa Norte, Brasília 70919-970, Brazil.

Gestão e Tecnologia Industrial (PPG), Centro Universitário SENAI CIMATEC, Salvador 41650-010, Brazil.

出版信息

Entropy (Basel). 2021 Nov 18;23(11):1530. doi: 10.3390/e23111530.

DOI:10.3390/e23111530
PMID:34828228
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8624125/
Abstract

We present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. We discuss why the infection rate, instead of the effective reproduction number or growth rate of cases, is a proper choice to perform this analysis when considering a wide span of time. We obtain a strong Spearman's rank order correlation between the social distancing metric and the infection rate in each locality. We show that mask mandates increase the values of Spearman's correlation in the United States, where a mandate was adopted. We also obtain an explicit numerical relation between the infection rate and the social distancing metric defined in the present work.

摘要

我们对美国和巴西所有州及人口最多的城市、22个欧洲经济共同体国家以及英国的数据进行了分析，以研究新冠病毒（SARS-CoV-2）感染率与一种社交距离指标之间的关系。我们讨论了为何在考虑较长时间段时，感染率而非病例的有效再生数或增长率是进行此分析的合适选择。我们在每个地区的社交距离指标与感染率之间获得了很强的斯皮尔曼等级相关。我们表明，在美国实施口罩强制令的地方，口罩强制令提高了斯皮尔曼相关性的值。我们还得到了感染率与本研究中定义的社交距离指标之间的明确数值关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/6d76aba23fc3/entropy-23-01530-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/89c341de3429/entropy-23-01530-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/e89a236ffe2d/entropy-23-01530-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/57e809b986f5/entropy-23-01530-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/6d76aba23fc3/entropy-23-01530-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/89c341de3429/entropy-23-01530-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/61dbe51655b5/entropy-23-01530-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/534e1107586f/entropy-23-01530-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/e89a236ffe2d/entropy-23-01530-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/57e809b986f5/entropy-23-01530-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/9a75adc1e8b1/entropy-23-01530-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c100/8624125/6d76aba23fc3/entropy-23-01530-g007.jpg

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Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations.新冠疫情的行为动态：评估92个国家的漏报情况、多波疫情及依从性疲劳
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Sci Rep. 2021 Jul 2;11(1):13717. doi: 10.1038/s41598-021-92766-z.
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